Распознавание жестов ручной азбуки ASL

A method is proposed and software is developed for automatic recognition of gestures usedin ASL fingerspelling. Static gestures are captured using the new generation 3D sensor AsusXtion Pro Live. Gesture recognition is achieved by extracting and further comparing thenormalized geometric skeletons of the hand. Hand skeletons are compared using DynamicTime Warping algorithm, which has polynomial complexity.Предложен метод и разработана программная система автоматического распознавания жестов ручной азбуки глухонемых ASL (American Sign Language). В качестве устройства ввода информации о статических жестах, отображающих цифры и латинские буквы, выступает трёхмерный сенсор нового поколения Asus Xtion Pro Live. Распознавание жестов осуществляется посредством извлечения, предварительной обработки и последующего сравнения нормализованных геометрических скелетов руки на основе анализа дальностных изображений, формируемых сенсором. Сравнение скелетов осуществляется на основе алгоритма динамической трансформации шкалы времени (Dynamic Time Warping, DTW), имеющего полиномиальную сложность

Gesture recognition in the problem of contactless control of an unmanned aerial vehicle

The problem of contactless control of an unmanned aerial vehicle by human gestures is considered. A system of commands is proposed for an AR.Drone 2.0 quadcopter equipped with a builtin computer, two color video cameras, the Linux operating system, and sensors for measuring the flight height, speed, and stability. An Asus Xtion Pro Live three-dimensional sensor based on triangulation and structured light is used to enter data into the control system. Gesture recognition is performed by frame-by-frame processing of a video sequence consisting of depth images. The method does not require initial training, is insensitive to changes in lighting, and is invariant to the sizes of the human palm and body. © 2015, Allerton Press, Inc

Gesture recognition in the problem of contactless control of an unmanned aerial vehicle

The problem of contactless control of an unmanned aerial vehicle by human gestures is considered. A system of commands is proposed for an AR.Drone 2.0 quadcopter equipped with a builtin computer, two color video cameras, the Linux operating system, and sensors for measuring the flight height, speed, and stability. An Asus Xtion Pro Live three-dimensional sensor based on triangulation and structured light is used to enter data into the control system. Gesture recognition is performed by frame-by-frame processing of a video sequence consisting of depth images. The method does not require initial training, is insensitive to changes in lighting, and is invariant to the sizes of the human palm and body. © 2015, Allerton Press, Inc

Automatic transformation of Russian manual-alphabet gestures into textual form

This paper considers the task of sign-language interpretation for gestures that are used in the Russian Manual Alphabet (RMA) for the deaf and dumb. A software-hardware system is suggested that permits static and dynamic gestures to be transformed into a text on-line; the conducted experiments show that the system currently provides a fairly reliable recognition of all static and some dynamic RMA gestures. Methods to improve the quality of the dynamic gesture analysis are planned. © 2014, Allerton Press, Inc